System and method for deploying interventional medical devices using magnetic resonance fingerprinting (mrf)

ABSTRACT

A method for target identification for a deep brain stimulation procedure includes acquiring a set of magnetic resonance fingerprinting (MRF) data for a region of interest in a subject using a MRI system, comparing the set of MRF data to an MRF dictionary to determine at least one parameter for the MRF data for the region of interest, generating a quantitative map of the at least one parameter, segmenting a target area of the region of interest based on the MRF data, generating at least one trajectory for placement of at least one electrode in the target area of the region of interest based on the segmentation of the target area and displaying the quantitative map and the at least one trajectory on a display.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based on, claims priority to, and incorporatesherein by reference in its entirety U.S. Ser. No. 62/768,177 filed Nov.16, 2018 and entitled “System and Method For Deploying InterventionalMedical Devices Using Magnetic Resonance Fingerprinting (MRF).”

BACKGROUND

Characterizing tissue species using nuclear magnetic resonance (“NMR”)can include identifying different properties of a resonant species(e.g., T1 spin-lattice relaxation, T2 spin-spin relaxation, protondensity). Other properties like tissue types and super-position ofattributes can also be identified using NMR signals. These propertiesand others may be identified simultaneously using magnetic resonancefingerprinting (“MRF”), which is described, as one example, by D. Ma, etal., in “Magnetic Resonance Fingerprinting,” Nature, 2013; 495 (7440):187-192.

Conventional magnetic resonance imaging (“MRI”) pulse sequences includerepetitive similar preparation phases, waiting phases, and acquisitionphases that serially produce signals from which images can be made. Thepreparation phase determines when a signal can be acquired anddetermines the properties of the acquired signal. For example, a firstpulse sequence may produce a T1-weighted signal at a given echo time(“TE”), while a second pulse sequence may produce a T2-weighted signalat a different (or second) TE. These conventional pulse sequencestypically provide qualitative results where data are acquired withvarious weighting or contrasts that highlight a particular parameter(e.g., T1 relaxation, T2 relaxation).

When magnetic resonance (“MR”) images are generated, they may be viewedby a radiologist and/or surgeon who interprets the qualitative imagesfor specific disease signatures. The radiologist may examine multipleimage types (e.g., T1-weighted, T2 weighted) acquired in multipleimaging planes to make a diagnosis. The radiologist or other individualexamining the qualitative images may need particular skill to be able toassess changes from session to session, from machine to machine, andfrom machine configuration to machine configuration.

Unlike conventional MRI, MRF employs a series of varied sequence blocksthat simultaneously produce different signal evolutions in differentresonant species (e.g., tissues) to which the radio frequency (“RF”) isapplied. The signals from different resonant tissues will, however, bedifferent and can be distinguished using MRF. The different signals canbe collected over a period of time to identify a signal evolution forthe volume. Resonant species in the volume can then be characterized bycomparing the signal evolution to known signal evolutions.Characterizing the resonant species may include identifying a materialor tissue type, or may include identifying MR parameters associated withthe resonant species. The “known” evolutions may be, for example,simulated evolutions calculated from physical principles and/orpreviously acquired evolutions. A large set of known evolutions may bestored in a dictionary. As mentioned, MRF permits simultaneousquantification of multiple tissue properties (e.g. T1 and T2).

Deep brain stimulation (DBS) is a neurosurgical therapy, analogous tocardiac pacemakers implanted in the brain, which is used to treat a widerange of neurological disorders, most notably Parkinson's disease (PD).For example, DBS has been used to control symptoms, such as rigidity,slowed movement, tremors, and walking difficulties in patients withParkinson's. Other applications for DBS therapy include, for example,epilepsy, chronic pain, obsessive compulsive disorder, depression, etc.

DBS involves the implantation of an electrical stimulator into a definedarea of a patient's brain, followed by delivery of high-frequencyelectrical impulses (e.g., up to 24 hours a day). Although the exactmechanism of action is not well understood, it is believed thatelectrical current produced by DBS interfere with or block brainactivity close to the activation site. DB S therapies are intimatelydependent upon accurate placement of the electrode(s) in the targetbrain region to achieve the desired therapeutic effects. However, mostDBS targets are small nuclei in the brain, which are difficult toidentify and visualize on a patient-specific basis using traditional MRIimaging. The most common surgical target for DBS is the subthalamicnucleus (STN) which is a structure with an approximate size of 10×8×5mm³ that is not reliably visible on traditional MRI images. Othersurgical targets for DBS include, for example, the vertralis internediusnucleus of the thalamus (VIM), the globus pallidus (GPi), etc. Onecommon surgical targeting method uses preoperative MRI data and a brainatlas and attempts to fit the brain atlas to MRI images of the patientto estimate the location of a surgical target (e.g., STN). DBS surgicaltargeting methods that use a brain atlas, however, fail to account forpatient to patient anatomical variability and inly provide an indirectestimate of the target in the patient's brain. Patient-to-patientvariability in brain anatomy is substantial and the mechanical accuracyof stereotactic neurosurgical frame systems suffers from errors on theorder of ˜1 mm. As a result, about 15% of DBS leads are misplaced andrequire a revision surgery to correct their positioning. In addition,while DBS therapies are capable of generating good clinical responses,the technology has remained relatively limited in its clinical use. Onemajor factor limiting the clinical growth of DBS is the lack ofstandardized and quantitatively validated methods for optimalpatient-specific electrode placement. Such advances could enable abroader range of neurosurgeons (and hospital centers) to successfullydeploy DBS therapies to their patients.

There is a need for a system and method to identify a DBS target (e.g.,STN) on a patient-specific basis with a high degree of anatomicalspecificity and quantitative accuracy.

SUMMARY OF THE DISCLOSURE

In accordance with an embodiment, a method for target identification fora deep brain stimulation procedure includes acquiring a set of magneticresonance fingerprinting (MRF) data for a region of interest in asubject using a MRI system, comparing the set of MRF data to an MRFdictionary to determine at least one parameter for the MRF data for theregion of interest, generating a quantitative map of the at least oneparameter, segmenting a target area of the region of interest based onthe MRF data, generating at least one trajectory for placement of atleast one electrode in the target area of the region of interest basedon the segmentation of the target area and displaying the quantitativemap and the at least one trajectory on a display.

In accordance with another embodiment, a system for targetidentification for a deep brain stimulation procedure, the systemincludes a stereotactic frame attached to a region of the subject and amagnetic resonance fingerprinting (MRF) system. The MRF system includesa magnet system configured to generate a polarizing magnetic field aboutat least a portion of a subject, a magnetic gradient system including aplurality of magnetic gradient coils configured to apply at least onemagnetic gradient field to the polarizing magnetic field, a radiofrequency (RF) system configured to apply an RF field to the subject andto receive magnetic resonance signals from the subject using a coilarray; a computer system and a display. The computer system isprogrammed to acquire a set of magnetic resonance fingerprinting (MRF)data for a region of interest in a subject, compare the set of MRF datato an MRF dictionary to determine at least one parameter for the MRFdata for the region of interest; generate a quantitative map of the atleast one parameter, segment a target area of the region of interestbased on the MRF data and generate at least one trajectory for placementof at least one electrode in the target area of the region of interestbased on the segmentation of the target area. The display is configuredfor displaying the quantitative map and the at least one trajectory.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will hereafter be described with reference to theaccompanying drawings, wherein like reference numerals denote likeelements.

FIG. 1 is a schematic diagram of an example MRI system in accordancewith an embodiment;

FIG. 2 is a block diagram of a system for identifying targets for deepbrain stimulation (DBS) and positioning DBS electrodes in accordancewith an embodiment;

FIG. 3 illustrates a method for identifying targets for DBS andpositioning DBS electrodes in accordance with an embodiment; and

FIG. 4 shows an example whole brain MRF T1 and T2 maps acquired with astereotactic neurological frame on a subject in accordance with anembodiment.

DETAILED DESCRIPTION

Magnetic resonance fingerprinting (“MRF”) is a technique thatfacilitates mapping of tissue or other material properties based onrandom or pseudorandom measurements of the subject or object beingimaged. In particular, MRF can be conceptualized as employing a seriesof varied “sequence blocks” that simultaneously produce different signalevolutions in different “resonant species” to which the RF is applied.The term “resonant species,” as used herein, refers to a material, suchas water, fat, bone, muscle, soft tissue, and the like, that can be madeto resonate using NMR. By way of illustration, when radio frequency(“RF”) energy is applied to a volume that has both bone and muscletissue, then both the bone and muscle tissue will produce a nuclearmagnetic resonance (“NMR”) signal; however, the “bone signal” representsa first resonant species and the “muscle signal” represents a secondresonant species, and thus the two signals will be different. Thesedifferent signals from different species can be collected simultaneouslyover a period of time to collect an overall “signal evolution” for thevolume.

The measurements obtained in MRF techniques are achieved by varying theacquisition parameters from one repetition time (“TR”) period to thenext, which creates a time series of signals with varying contrast.Examples of acquisition parameters that can be varied include flip angle(“FA”), RF pulse phase, TR, echo time (“TE’), and sampling patterns,such as by modifying one or more readout encoding gradients. Theacquisition parameters are varied in a random manner, pseudorandommanner, or other manner that results in signals from different materialsor tissues to be spatially incoherent, temporally incoherent, or both.For example, in some instances, the acquisition parameters can be variedaccording to a non-random or non-pseudorandom pattern that otherwiseresults in signals from different materials or tissues to be spatiallyincoherent, temporally incoherent, or both.

From these measurements, which as mentioned above may be random orpseudorandom, or may contain signals from different materials or tissuesthat are spatially incoherent, temporally incoherent, or both, MRFprocesses can be designed to map any of a wide variety of parameters.Examples of such parameters that can be mapped may include, but are notlimited to, tissue parameters or properties such as longitudinalrelaxation time (T₁), transverse relaxation time (T₂), and protondensity (ρ) and device dependent parameters such as main or staticmagnetic field map (B₀). MRF is generally described in U.S. Pat. No.8,723,518 and Published U.S. Patent Application No. 2015/0301141, eachof which is incorporated herein by reference in its entirety.

The data acquired with MRF techniques are compared with a dictionary ofsignal models, or templates, that have been generated for differentacquisition parameters from magnetic resonance signal models, such asBloch equation-based physics simulations. This comparison allowsestimation of the physical parameters, such as those mentioned above. Asan example, the comparison of the acquired signals to a dictionary canbe performed using any suitable matching or pattern recognitiontechnique. The parameters for the tissue or other material in a givenvoxel are estimated to be the values that provide the best signaltemplate matching. For instance, the comparison of the acquired datawith the dictionary can result in the selection of a signal vector,which may constitute a weighted combination of signal vectors, from thedictionary that best corresponds to the observed signal evolution. Theselected signal vector includes values for multiple differentquantitative parameters, which can be extracted from the selected signalvector and used to generate the relevant quantitative parameter maps.

The stored signals and information derived from reference signalevolutions may be associated with a potentially very large data space.The data space for signal evolutions can be partially described by:

$\begin{matrix}{{{SE} = {\sum\limits_{s = 1}^{N_{S}}\; {\prod\limits_{i = 1}^{N_{A}}\; {\sum\limits_{j = 1}^{N_{RF}}\; {{R_{i}(\alpha)}{R_{{RF}_{ij}}\left( {\alpha,\varphi} \right)}{R(G)}{E_{i}\left( {T_{1},T_{2},D} \right)}M_{0}}}}}};} & (1)\end{matrix}$

where SE is a signal evolution; N_(S) is a number of spins; N_(A) is anumber of sequence blocks; N_(RF) is a number of RF pulses in a sequenceblock; α is a flip angle; ϕ is a phase angle; R_(i)(α) is a rotation dueto off resonance; R_(RF) _(ij) (α,ϕ) is a rotation due to RFdifferences; R(G) is a rotation due to a magnetic field gradient; T₁ isa longitudinal, or spin-lattice, relaxation time; T₂ is a transverse, orspin-spin, relaxation time; D is diffusion relaxation; E_(i)(T₁,T₂,D) isa signal decay due to relaxation differences; and M₀ is themagnetization in the default or natural alignment to which spins alignwhen placed in the main magnetic field.

While E_(i)(T₁,T₂,D) is provided as an example, in different situations,the decay term, E_(i)(T₁, T₂,D), may also include additional terms,E_(i)(T₁,T₂, . . . ) or may include fewer terms, such as by notincluding the diffusion relaxation, as E_(i)(T₁,T₂) or E_(i)(T₁,T₂, . .. ). Also, the summation on “j” could be replace by a product on “j”.The dictionary may store signals described by,

S _(i) =R _(i) E _(i)(S _(i−1))  (2);

where S₀ is the default, or equilibrium, magnetization; S_(i) is avector that represents the different components of magnetization, M_(x),M_(y), and M_(z) during the i^(th) acquisition block; R_(i) is acombination of rotational effects that occur during the i^(th)acquisition block; and E_(i) is a combination of effects that alter theamount of magnetization in the different states for the i^(th)acquisition block. In this situation, the signal at the i^(th)acquisition block is a function of the previous signal at acquisitionblock (i.e., the (i−1)^(th) acquisition block). Additionally oralternatively, the dictionary may store signals as a function of thecurrent relaxation and rotation effects and of previous acquisitions.Additionally or alternatively, the dictionary may store signals suchthat voxels have multiple resonant species or spins, and the effects maybe different for every spin within a voxel. Further still, thedictionary may store signals such that voxels may have multiple resonantspecies or spins, and the effects may be different for spins within avoxel, and thus the signal may be a function of the effects and theprevious acquisition blocks.

Thus, in MRF, a unique signal timecourse is generated for each pixel.This timecourse evolves based on both physiological tissue propertiessuch as T1 or T2 as well as acquisition parameters like flip angle (FA)and repetition time (TR). This signal timecourse can, thus, be referredto as a signal evolution and each pixel can be matched to an entry inthe dictionary, which is a collection of possible signal evolutions ortimecourses calculated using a range of possible tissue property valuesand knowledge of the quantum physics that govern the signal evolution.Upon matching the measured signal evolution/timecourse to a specificdictionary entry, the tissue properties corresponding to that dictionaryentry can be identified. A fundamental criterion in MRF is that spatialincoherence be maintained to help separate signals that are mixed due toundersampling. In other words, signals from various locations shoulddiffer from each other, in order to be able to separate them whenaliased.

To achieve this process, a magnetic resonance imaging (MRI) system ornuclear magnetic resonance (NMR) system may be utilized. FIG. 1 shows anexample of an MRI system 100 in accordance with an embodiment. MRIsystem 100 may be used to implement the methods described herein. MRIsystem 100 includes an operator workstation 102, which may include adisplay 104, one or more input devices 106 (e.g., a keyboard, a mouse),and a processor 108. The processor 108 may include a commerciallyavailable programmable machine running a commercially availableoperating system. The operator workstation 102 provides an operatorinterface that facilitates entering scan parameters into the MRI system100. The operator workstation 102 may be coupled to different servers,including, for example, a pulse sequence server 110, a data acquisitionserver 112, a data processing server 114, and a data store server 116.The operator workstation 102 and the servers 110, 112, 114, and 116 maybe connected via a communication system 140, which may include wired orwireless network connections.

The pulse sequence server 110 functions in response to instructionsprovided by the operator workstation 102 to operate a gradient system118 and a radiofrequency (“RF”) system 120. Gradient waveforms forperforming a prescribed scan are produced and applied to the gradientsystem 118, which then excites gradient coils in an assembly 122 toproduce the magnetic field gradients G_(x), G_(y), and G_(z) that areused for spatially encoding magnetic resonance signals. The gradientcoil assembly 122 forms part of a magnet assembly 124 that includes apolarizing magnet 126 and a whole-body RF coil 128.

RF waveforms are applied by the RF system 120 to the RF coil 128, or aseparate local coil to perform the prescribed magnetic resonance pulsesequence. Responsive magnetic resonance signals detected by the RF coil128, or a separate local coil, are received by the RF system 120. Theresponsive magnetic resonance signals may be amplified, demodulated,filtered, and digitized under direction of commands produced by thepulse sequence server 110. The RF system 120 includes an RF transmitterfor producing a wide variety of RF pulses used in MRI pulse sequences.The RF transmitter is responsive to the prescribed scan and directionfrom the pulse sequence server 110 to produce RF pulses of the desiredfrequency, phase, and pulse amplitude waveform. The generated RF pulsesmay be applied to the whole-body RF coil 128 or to one or more localcoils or coil arrays.

The RF system 120 also includes one or more RF receiver channels. An RFreceiver channel includes an RF preamplifier that amplifies the magneticresonance signal received by the coil 128 to which it is connected, anda detector that detects and digitizes the I and Q quadrature componentsof the received magnetic resonance signal. The magnitude of the receivedmagnetic resonance signal may, therefore, be determined at a sampledpoint by the square root of the sum of the squares of the I and Qcomponents:

M=√{square root over (I ² +Q ²)}  (3);

and the phase of the received magnetic resonance signal may also bedetermined according to the following relationship:

$\begin{matrix}{\phi = {{\tan^{- 1}\left( \frac{Q}{I} \right)}.}} & (4)\end{matrix}$

The pulse sequence server 110 may receive patient data from aphysiological acquisition controller 130. By way of example, thephysiological acquisition controller 130 may receive signals from anumber of different sensors connected to the patient, includingelectrocardiograph (“ECG”) signals from electrodes, or respiratorysignals from a respiratory bellows or other respiratory monitoringdevices. These signals may be used by the pulse sequence server 110 tosynchronize, or “gate,” the performance of the scan with the subject'sheart beat or respiration.

The pulse sequence server 110 may also connect to a scan room interfacecircuit 132 that receives signals from various sensors associated withthe condition of the patient and the magnet system. Through the scanroom interface circuit 132, a patient positioning system 134 can receivecommands to move the patient to desired positions during the scan.

The digitized magnetic resonance signal samples produced by the RFsystem 120 are received by the data acquisition server 112. The dataacquisition server 112 operates in response to instructions downloadedfrom the operator workstation 102 to receive the real-time magneticresonance data and provide buffer storage, so that data is not lost bydata overrun. In some scans, the data acquisition server 112 passes theacquired magnetic resonance data to the data processor server 114. Inscans that require information derived from acquired magnetic resonancedata to control the further performance of the scan, the dataacquisition server 112 may be programmed to produce such information andconvey it to the pulse sequence server 110. For example, duringpre-scans, magnetic resonance data may be acquired and used to calibratethe pulse sequence performed by the pulse sequence server 110. Asanother example, navigator signals may be acquired and used to adjustthe operating parameters of the RF system 120 or the gradient system118, or to control the view order in which k-space is sampled. In stillanother example, the data acquisition server 112 may also processmagnetic resonance signals used to detect the arrival of a contrastagent in a magnetic resonance angiography (“MRA”) scan. For example, thedata acquisition server 112 may acquire magnetic resonance data andprocesses it in real-time to produce information that is used to controlthe scan.

The data processing server 114 receives magnetic resonance data from thedata acquisition server 112 and processes the magnetic resonance data inaccordance with instructions provided by the operator workstation 102.Such processing may include, for example, reconstructing two-dimensionalor three-dimensional images by performing a Fourier transformation ofraw k-space data, performing other image reconstruction algorithms(e.g., iterative or backprojection reconstruction algorithms), applyingfilters to raw k-space data or to reconstructed images, generatingfunctional magnetic resonance images, or calculating motion or flowimages.

Images reconstructed by the data processing server 114 are conveyed backto the operator workstation 102 for storage. Real-time images may bestored in a data base memory cache, from which they may be output tooperator display 102 or a display 136. Batch mode images or selectedreal time images may be stored in a host database on disc storage 138.When such images have been reconstructed and transferred to storage, thedata processing server 114 may notify the data store server 116 on theoperator workstation 102. The operator workstation 102 may be used by anoperator to archive the images, produce films, or send the images via anetwork to other facilities.

The MRI system 100 may also include one or more networked workstations142. For example, a networked workstation 142 may include a display 144,one or more input devices 146 (e.g., a keyboard, a mouse), and aprocessor 148. The networked workstation 142 may be located within thesame facility as the operator workstation 102, or in a differentfacility, such as a different healthcare institution or clinic.

The networked workstation 142 may gain remote access to the dataprocessing server 114 or data store server 116 via the communicationsystem 140. Accordingly, multiple networked workstations 142 may haveaccess to the data processing server 114 and the data store server 116.In this manner, magnetic resonance data, reconstructed images, or otherdata may be exchanged between the data processing server 114 or the datastore server 116 and the networked workstations 142, such that the dataor images may be remotely processed by a networked workstation 142.

The present disclosure describes a system and method that uses MRF forDBS surgical targeting to identify a target area of the brain (e.g.,STN, VIM, and GPi) on a patient-specific basis. MRF provides improvedanatomical specificity and quantitative accuracy for identifying thetarget area of the brain which allows accurate placement of electrodesin the target area of the brain. MRF may be used to provide astandardized image processing platform that simplifies DBS surgicaltargeting. MRF provides an improved dataset for DBS surgical targetingthat may be acquired in less time than traditional MRI scan. Inaddition, MRF eliminates the need for use of a brain atlas to identify aDB S surgical target. MRF may be used to provide a quantitativestandardization of MRI-based segmentation for patient-specific DBStarget identification. MRF may be used to provide a reproducible andquantitative identification of a target areas, such as STN, and isdirectly measured from the patient.

FIG. 2 is a block diagram of a system for identifying target areas fordeep brain stimulation (DBS) and positioning DBS electrodes inaccordance with an embodiment. In FIG. 2, the system 200 includes an MRIsystem 202, a stereotactic neurosurgical frame 206 and a deep brainstimulation system 208. MRI system 202 may be a system such as, forexample, MRI system 100 (shown in FIG. 1) that is configured to performMRF acquisitions, imaging and mapping as described above. Stereotacticframe 206 is attached to a region of a subject 204, for example, for aDBS procedure the stereotactic frame is attached to a patient's head.The stereotactic frame 206 is used to provide a three dimensionalcoordinate system for spatialized localization in reference to a targetimage and may be used to determine coordinates of a target area withinthe region of interest. For a DBS procedure, the region of interest isthe brain and the target area may be, for example, the subthalamicnucleus (STN), the vertralis internedius nucleus of the thalamus (VIM),the globus pallidus (GPi), etc. MRI system 202 may be used to acquireMRF data of a region of interest of the subject 204 and the stereotacticframe 206 as discussed further below with respect to FIG. 3. The MRFdata may then be used to generate parameter maps and images that may beused in identifying and segmenting a target area (e.g., STN) of theregion of interest (e.g., the brain) of the subject 204.

The identified target area may then be used to guide the placement ofstimulators 212 of a deep brain stimulation system 208 in the region ofinterest (i.e., brain) of the subject 204. For example, the identifiedtarget area and coordinates relative to the stereotactic frame may beused to develop a surgical plan that includes, for example, trajectoriesfor the placement of an electrode in the target area (e.g., STN) of thebrain. Deep brain stimulation system 208 includes a stimulation assembly210 and a controller 214. The stimulation assembly 210 may include aplurality of stimulators 212 configured to deliver stimulations tocontrol brain activity in the subject 204. The stimulators 212 mayinclude various electrodes or probes with electrical contacts configuredfor delivering electrical stimulations to the subject 204. In addition,the stimulation assembly 210 may also include various detectors orsensors capable of measuring brain activity in the subject 204.Stimulators 212 may be wholly or partially implanted in the target areaof the brain of the subject 204. The controller 214 may be configured toperform a variety of functions for operating the stimulation assembly210 such as, for example, sending and receiving instructions andoperational parameters, the storage of operational or stimulationparameters and instructions to memory, and providing (e.g., with asignal generator) activating or command signals to the stimulators 212to deliver electrical stimulations to various brain regions or tissuesof the subject. Controller 214 may provide stimulation signals withvarious intensities, frequencies, phases, pulse widths, durations andwaveforms. Controller 214 may include various connections, or terminals118 for transmitting signals. The controller 214 may also be configuredto detect brain signals acquired using the stimulation assembly 210. Forexample, the controller 214 may receive signals corresponding to brainactivity in the one or more regions of the subject's brain.

FIG. 3 illustrates a method for identifying target areas for DBS andpositioning DBS electrodes in accordance with an embodiment. At block302, a stereotactic frame (e.g., stereotactic frame 206 shown in FIG. 2)is attached to a region of a subject 204, for example, for a DBSprocedure the stereotactic frame is attached to a patient's head. Asdiscussed above, the stereotactic frame 206 is used to provide a threedimensional coordinate system for spatialized localization in referenceto a target image and may be used to determine coordinates of a targetarea within the region of interest. As discussed above, for a DBSprocedure the region of interest is the brain and the target area maybe, for example, the subthalamic nucleus (STN), the vertralisinternedius nucleus of the thalamus (VIM), the globus pallidus (GPi),etc. At block 304, an MRF dictionary is accessed. The MRF dictionary maybe stored in memory or data storage of, for example, an MRI system(e.g., the MRI system 100 of FIG. 1) or other computer system. As usedherein, the term “accessing” may refer to any number of activitiesrelated to generating, retrieving or processing the MRF dictionaryusing, for example, MRI system 100 (shown in FIG. 1), an externalnetwork, information repository, or combinations thereof. The MRFdictionary includes known signal evolutions (e.g., simulated signalevolutions). In an embodiment, the MRF dictionary may be generated usinga Bloch simulation. At block 306, MRF data is acquired from tissue in aregion of interest (e.g., the brain) in a subject using, for example, anMRI system (e.g., MRI system 100 shown in FIG. 1). Acquiring MRF datamay include, for example, performing a pulse sequence using a series ofvaried sequence blocks to elicit a series of signal evolutions from atissue in the region of interest. The MRF data may be acquired using apulse sequence such as, for example, Fast Imaging with Steady-State FreePrecession (FISP), FLASH, TrueFISP, gradient echo, spin echo, etc. Theacquired MRF data may be stored in memory or data storage of, forexample, an MRI system (e.g., the MRI system 100 of FIG. 1) or othercomputer system. In various embodiments, MRF data may be acquired before(i.e., preoperatively) a procedure to implant DBS electrodes in thebrain of the subject, during (i.e., intraoperatively) the procedure orboth before and during the procedure. As discussed further below, theMRF data acquired before and/or after the procedure is used to identifythe target areas and guide the placement of electrodes.

The MRF data acquired at block 306 is stored and compared to the MRFdictionary at block 308 to match the acquired signal evolutions withsignal evolutions stored in the MRF dictionary. Comparing the MRF datato the MRF dictionary may be performed in a number of ways such as, forexample, using a pattern matching, template matching or other matchingalgorithm. In one embodiment, the inner products between the normalizedmeasured time course of each pixel and all entries of the normalizeddictionary are calculated, and the dictionary entry corresponding to themaximum value of the inner product is taken to represent the closestsignal evolution to the acquired signal evolution. At block 310, one ormore parameters of the MRF data are determined based on the comparisonand matching at block 308. The parameters may include, for example,tissue parameters or properties such as longitudinal relaxation time(T1), transverse relaxation time (T2), and proton density (ρ) and devicedependent parameters such as main or static magnetic field (B₀).

At block 312, images or maps may be generated indicating at least one ofthe identified parameters for the tissue in the region of interest inthe subject. For example, a map may be generated having a quantitativeindication of the at least one parameter. The images or maps may beprovided and displayed on a display (e.g., display 104, 136 or 144 shownin FIG. 1). The images or maps may show markers from the stereotacticframe that may be used to determine coordinates of the target area(e.g., STN) of the region of interest in the subject. FIG. 4 shows anexample whole brain MRF T1 and T2 maps acquired from a subject having anattached stereotactic neurological frame in accordance with anembodiment. FIG. 4 shows an example quantitative T1 map 402 and anexample quantitative T2 map 404. In this example, the MRF data wasacquired with a 3T MRI scanner and used to generate a fully quantitative3D image of a while human brain with a MRI compatible stereotacticframe. The quantitative T1 402 and T2 404 maps were created at 1.2 mmisotropic resolution. Basic tissue clusters were calculated usingk-means analysis and used to segment anatomical structures within thesubthalamic region. In this example, the whole brain MF scan time wasless than 12 minutes, including a B1 mapping scan to correct forinhomogeneity.

Returning to FIG. 3, at block 314, the target area of the region ofinterest is identified or segmented based on the MRF data. For example,the STN may be identified segmented using the acquired MRF data. In oneembodiment, machine learning techniques may be used to identify thetarget region (e.g., the STN) in the MRF data. For example, a neuralnetwork may be trained using training MRF datasets for imaging of thebrain such as, for example, for normal subjects and subjects withParkinson's. In on embodiment, traditional brain atlas templates may becoupled to the MRF data to establish training information on the generallocation of the STN in the brain. The unique quantitative nature of theindividual voxels may then be used to define the specific T1, T2, B0,etc. values that correspond to STN tissue. The MRF data acquired atblock 306 or the images or parameter maps generated at block 312 mayinput to the trained neural network to identify the STN for the specificpatient. In various embodiments, the separate trained neural networksmay be created and customized for different subsets of patient, forexample, for tissue characteristics of an old brain (i.e., >65 yearsold) or even more specifically for old PD brains. In another embodiment,the target region (e.g., STN) of the brain may be identified orsegmented based on the MRF data using known segmentation techniques. Inanother embodiment, known segmentation techniques may be used toidentify anatomical structures within the target region. For example,k-means analysis may be used to calculate basic tissue clusters that maybe used to segment anatomical structures with the STN. In addition toidentification or segmentation of the target area in the brain,coordinates of the target area of the brain may also be determined. Asdiscussed above, markers from the stereotactic frame may be used todetermine the coordinates of a target area within the region ofinterest.

At block 316, a plan (e.g., a trajectory) for positioning of theelectrodes is generated based on the identified target region,coordinates and the generated maps or images. The surgical plan mayinclude, for example, trajectories that indicate the depth an angle atwhich an electrode should be positioned in the target area of the brain.At block 320, the images, maps and surgical plans may be displayed on adisplay such as, for example, display 104, 136 or 144 shown in FIG. 1.At block 320, the electrodes may be positioned in the target area of theregion of interest based on the surgical plan. The electrodes may beplaced in the brain by, for example, a neurosurgeon performing theprocedure. In one embodiment, the electrodes may be placed on both theleft and right sides of the brain. The electrodes (e.g., stimulators 212shown I FIG. 2) are placed in the target area of the brain through smallholes made at the top of the skull of the patient. Typically, theelectrodes are connected by long wires that travel under the skin anddown the neck to a battery-powered stimulator (e.g., controller 214shown in FIG. 2) positioned under the skin of the chest.

Computer-executable instructions for identifying target areas for deepbrain stimulation and positioning deep brain stimulation electrodesaccording to the above-described methods may be stored on a form ofcomputer readable media. Computer readable media includes volatile andnonvolatile, removable, and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer readable media includes, but is not limited to, random accessmemory (RAM), read-only memory (ROM), electrically erasable programmableROM (EEPROM), flash memory or other memory technology, compact disk ROM(CD-ROM), digital volatile disks (DVD) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storethe desired instructions and which may be accessed by a system (e.g., acomputer), including by internet or other computer network form ofaccess.

The present invention has been described in terms of one or morepreferred embodiments, and it should be appreciated that manyequivalents, alternatives, variations, and modifications, aside fromthose expressly states, are possible and within the scope of theinvention. The order and sequence of any process or method steps may bevaried or re-sequenced according to alternative embodiments.

1. A method for target identification for a deep brain stimulationprocedure, the method comprising: acquiring a set of magnetic resonancefingerprinting (MRF) data for a region of interest in a subject using aMRI system; comparing the set of MRF data to an MRF dictionary todetermine at least one parameter for the MRF data for the region ofinterest; generating a quantitative map of the at least one parameter;segmenting a target area of the region of interest based on the MRFdata; generating at least one trajectory for placement of at least oneelectrode in the target area of the region of interest based on thesegmentation of the target area; and displaying the quantitative map andthe at least one trajectory on a display.
 2. The method according toclaim 1, further comprising positioning at least one electrode in thetarget area based on the at least one trajectory.
 3. The methodaccording to claim 1, wherein the region of interest is the brain andthe target area is the subthalamic nucleus (STN).
 4. The methodaccording to claim 1, wherein generating at least one trajectory forplacement of at least one electrode in the target area includesdetermining a set of coordinates for the target area based on a set ofmarkers from a stereotactic frame attached to the subject.
 5. The methodaccording to claim 1, wherein segmenting a target area of the region ofinterest include using a trained neural network to segment the targetarea.
 6. The method according to claim 1, wherein the region of interestis the brain and the target area is the vertralis internedius nucleus ofthe thalamus (VIM).
 7. The method according to claim 1, wherein theregion of interest is the brain and the target area is the globuspallidus (GPi).
 8. The method according to claim 1, wherein the at leastone parameter is T1.
 9. The method according to claim 1, wherein the atleast one parameter is T2.
 10. A system for target identification for adeep brain stimulation procedure, the system comprising: a stereotacticframe attached to a region of the subject; a magnetic resonancefingerprinting (MRF) system comprising: a magnet system configured togenerate a polarizing magnetic field about at least a portion of asubject; a magnetic gradient system including a plurality of magneticgradient coils configured to apply at least one magnetic gradient fieldto the polarizing magnetic field; a radio frequency (RF) systemconfigured to apply an RF field to the subject and to receive magneticresonance signals from the subject using a coil array; and a computersystem programmed to: acquire a set of magnetic resonance fingerprinting(MRF) data for a region of interest in a subject; compare the set of MRFdata to an MRF dictionary to determine at least one parameter for theMRF data for the region of interest; generate a quantitative map of theat least one parameter; segment a target area of the region of interestbased on the MRF data; and generate at least one trajectory forplacement of at least one electrode in the target area of the region ofinterest based on the segmentation of the target area; and a display fordisplaying the quantitative map and the at least one trajectory.
 11. Thesystem according to claim 10, wherein the region of interest is thebrain and the target area is the subthalamic nucleus (STN).
 12. Thesystem according to claim 10, wherein the region of interest is thebrain and the target area is the vertralis internedius nucleus of thethalamus (VIM).
 13. The system according to claim 10, wherein the regionof interest is the brain and the target area is the globus pallidus(GPi).
 14. The system according to claim 1, wherein the computer systemis further programmed to generating determine a set of coordinates forthe target area based on a set of markers from the stereotactic frameattached to the subject.
 15. The system according to claim 10, whereinthe computer system is further programmed to segment the target area ofthe region of interest using a trained neural network.
 16. The systemaccording to claim 10, wherein the at least one parameter is T1.
 17. Thesystem according to claim 10, wherein the at least one parameter is T1.